Master the Credit Application Scorecard: Predictive Models for Lending (AMER)

Presented by

Chris Long, VP of Worldwide Data Analytics Solutions, Altair

About this talk

During this session, we will discuss: 1. How to Build & Understand Scorecards • Build, assess, and monitor machine learning models in an intuitive drag-and-drop interface or the SAS language, R, and Python code • Enable your team to easily generate credit applicant predictions by developing scorecards, collection models, and Basel reports • Manage third-party data – delinquency scores, failure scores, payment ratings, demographics, historical account activity, etc. – to know the probability of loan default and minimize your organization’s risk. 2. How to Deploy Predictive Models with Confidence • Deploy models (via Cloud, server, or local) as APIs for real-time and on-demand applications • Track performance to ensure model currency • Integrate directly with common third party applications like Fiserv, Jack Henry, Black Knight, Sagent, Loan Sphere, Equifax, Experian, and more • Import and export models built in Python, R, or the SAS language

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Altair is a global technology company that provides software and cloud solutions in the areas of data analytics, simulation, and high-performance computing (HPC). Altair Data Analytics enables people of all skill levels to access, generate, and use smart data to make insightful, informed decisions. Our no-code data transformation, AI/ML, and data visualization tools reduce the complexities often encountered in data analytics. We eliminate the need for specialized programming knowledge and democratize the analytics process. As a result, people across the business can leverage the value of insightful analytics. Visit to learn more!